Contextual Graph Markov Model (CGMM) is an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in an efficient and scalable way across the graph vertexes and edges. The resulting graph encoding is used in combination with discriminative models to address structure classification benchmarks.

Description and image from: [Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing](https://arxiv.org/pdf/1805.10636.pdf)

**GridMask** is a data augmentation method that randomly removes some pixels of an input image. Unlike other methods, the region that the algorithm removes is neither a continuous region nor random pixels in dropout. Instead, the algorithm removes a region with disconnected pixel sets, as shown in the Figure.

We express the setting as

$$
\tilde{\mathbf{x}}=\mathbf{x} \times M
$$

where $\mathbf{x} \in R^{H \times W \times C}$ represents the input image, $M \in$ $\{0,1\}^{H \times W}$ is the binary mask that stores pixels to be removed, and $\tilde{\mathbf{x}} \in R^{H \times W \times C}$ is the result produced by the algorithm. For the binary mask $M$, if $M_{i, j}=1$ we keep pixel $(i, j)$ in the input image; otherwise we remove it. GridMask is applied after the image normalization operation.

The shape of $M$ looks like a grid, as shown in the Figure . Four numbers $\left(r, d, \delta_{x}, \delta_{y}\right)$ are used to represent a unique $M$. Every mask is formed by tiling the units. $r$ is the ratio of the shorter gray edge in a unit. $d$ is the length of one unit. $\delta\_{x}$ and $\delta\_{y}$ are the distances between the first intact unit and boundary of the image.

GridMask

GridMask Data Augmentation

CGMM

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

An **EESP Unit**, or  Extremely Efficient Spatial Pyramid of Depth-wise Dilated Separable Convolutions, is an image model block designed for edge devices. It was proposed as part of the [ESPNetv2](https://paperswithcode.com/method/espnetv2) CNN architecture. 

This building block is based on a reduce-split-transform-merge strategy. The EESP unit first projects the high-dimensional input feature maps into low-dimensional space using groupwise pointwise convolutions and then learns the representations in parallel using depthwise dilated separable convolutions with different dilation rates. Different dilation rates in each branch allow the EESP unit to learn the representations from a large effective receptive field. To remove the gridding artifacts caused by dilated convolutions, the EESP fuses the feature maps using [hierarchical feature fusion](https://paperswithcode.com/method/hierarchical-feature-fusion) (HFF).

Source	Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com